Web traffic routing

ABSTRACT

Aspects of the disclosure provide for methods, systems, and apparatuses, including computer storage media, for adaptively routing web traffic. A system can receive from a user computing device accessing a web resource, a content request, including data characterizing user interaction by the user computing device with the web resource before or after receiving the first content request. The system can identify a policy from a plurality of policies to execute in accordance with one or more objectives, wherein the plurality of policies are generated using data from a plurality of content requests and data characterizing user interaction with the web resource after serving a respective response to each of the plurality of content requests. The system can perform, in response to the content request, one or more actions of the ranked list of actions of the identified policy.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. Pat. Application No.17/358,680, filed on Jun. 25, 2021, which issued as U.S. Pat. No.11,489,908, the disclosures of which are hereby incorporated herein byreference.

BACKGROUND

Users may interact with a web page or resource through differentapplications, such as through a mobile browser, or an embedded browserof a separate mobile application. The application used by a particularuser to access web pages or resources may be dependent on the operatingsystem of the device, the applications installed on the device, or thedevice itself. The web page or resource can include content that a usercan interact with through a user interface, such as by clicking on alink. A link can represent an element of the web page that wheninteracted with, causes the device of the user to send a contentrequest.

SUMMARY

This disclosure provides for systems, methods, media, and devices foradaptively determining a policy by which content is provided to acomputing device in response to a content request. The policy can bespecific to characteristics of the computing device making the contentrequest and/or the user operating the computing device, as examples. Byleveraging diverse characteristics of many users and their respectivedevices requesting content, a feedback engine of a traffic routingsystem implemented according to aspects of this disclosure can specifyhow or what content is provided to different users in response to acontent request.

The response to a content request can be determined based on analyzingpatterns of content requests and responses from previousrequest-response interactions, and selecting a response for a currentcontent request most like previous content requests with characteristicssimilar to those of the current request. When multiple responses arepossible for a content request, a system as described herein canevaluate previous request-response interactions, and select a responseaccording to one or more compared metrics between each of the previouslyevaluated request-response interactions.

In some cases, however, the content received according to a global setof rules in response to a content request may not be the response aparticular user expects or wants. For example, a request may besubmitted by a user via selection of a link. The response to therequests may be a prompt on a user device to install a mobileapplication. This response may not be the response of a user whoselected the link from that same mobile application already installed ontheir device. Routing different types of content in response to requestsfrom individual users accessing the same link is a technical challenge,especially given that the same web page including the link can beaccessed by tens of thousands of different users in a given day. Inaddition, what is considered the response a user may expect or want canvary over time and according to different variables, such ascharacteristics of the user, characteristics of the user device, andcharacteristics of content providers and/or publishers of the requestedcontent.

A user computing device can send a content request, for example inresponse to receiving user input interacting with a link. The feedbackengine can process the content request and obtain data related to thecontent request. This data may include information related to the userdevice, the user, the publisher publishing the accessed web page orresource, and/or the content provider associated with the contentrequested. The feedback engine can process the obtained data anddetermine, according to one or more objectives, what content should beprovided to the user device in response to the content request. In thisway, the feedback engine can route content to the user device, accordingto the one or more objectives.

The feedback engine can process incoming content requests and providecontent that most satisfies the one or more objectives. The objectivescan be unique to the publisher publishing the web page or resourceprovided to the user computing device, and/or be unique and specified bythe content provider for which content is content requested by the userdevice. The one or more objectives can include maximizing conversions bythe user device, either to another web page or resource or to install aparticular application. The objective(s) in addition or alternativelycan include promoting user engagement with a particular type of content,or promoting certain types of user behavior, such as repeat purchasing.

One aspect of the disclosure provides for receiving, by one or moreprocessors and from a user computing device accessing a web resource, afirst content request, wherein the first content request includes datacharacterizing user interaction by the user computing device with theweb resource before or after receiving the first content request;identifying, by the one or more processors, a policy from a plurality ofpolicies including a ranked list of actions to perform in accordancewith one or more objectives; and performing, by the one or moreprocessors and in response to the content request, one or more actionsof the ranked list of actions of the identified policy.

In one example, the plurality of policies are generated using data froma plurality of content requests received by the one or more processorsand data characterizing user interaction with the web resource afterserving a respective response to each of the plurality of contentrequests.

In some instances, receiving the content request includes receiving adata object including a plurality of data points, each data pointcorresponding to one or more of: a user of the user computing device,the user computing device, a publisher publishing the web resourceaccessed by the user computing device, interactions between the usercomputing device and a publisher computing device hosting the webresource, and content provided in response to the request.

In some instances, the plurality of content requests are a plurality offirst content requests. The method can further include updating theplurality of policies, including receiving data corresponding to aplurality of second content requests previously received and processedby the one or more processors to generate a plurality of secondresponses; and generating, from the data corresponding to the pluralityof second content requests, a mapping between input data points of theplurality of second content requests to one or more mapped policies.

In some instances, the generating includes evaluating metrics from datapoints representing user interaction between a respective computingdevice and the web resource for different actions performed as part ofthe second content requests; determining actions of policies with thehighest evaluated metrics according to the one or more objectives; andmapping respective input data points to the policies including thedetermined actions.

In some instances, the one or more objectives include one or more ofincreasing user engagement, conversion rate, and repeat requests for therequested content. The evaluated metrics can include metricsquantifying, from the data characterizing user interaction at thecomputing device with the web resource before or after receiving thefirst content request, one or more of user engagement and conversionrate for the requested content.

In some instances, the ranked list of actions includes one or more of:prompting the user computing device to install a mobile application;redirecting the user computing device to a web resource hosting contentin response to the content request; and sending a deep link to the webresource hosting content in response to the content request.

In some instances, performing the one or more actions includes:determining if a first ranked action ranked higher than a second rankedaction of the ranked list can be performed to cause the user computingdevice to receive the content in response to the request; and performingthe second ranked action only if the first ranked action cannot beperformed.

In some instances, the user computing device is a first user computingdevice, the one or more actions are one or more first actions, and thecontent request is a first content request. The method further includesreceiving, by the one or more processors, a second content request froma second user computing device different from the first user computingdevice for content that is the same as requested content in the firstcontent request; determining whether to perform one or more secondactions different from the one or more first actions, based at least onrespective different device information specified in the first andsecond content requests for the first and second user computing devices,and in response to the determination, performing the one or more secondactions in response to the second content request.

In some instances, the one or more second actions are from a policydifferent from the identified policy generated using the one or moreobjectives.

Other examples of the foregoing aspect can include a system includingone or more processors, and one or more computer programs recorded onone or more computer-readable storage media.

Another aspect of the disclosure provides for A system including: one ormore processors, and one or more memory devices storing instructionsthat when executed by the one or more processors, cause the one or moreprocessors to perform operations including: receiving, from a usercomputing device accessing a web resource, a first content request,wherein the first content request includes data characterizing userinteraction by the user computing device with the web resource before orafter receiving the first content request; identifying, a policy from aplurality of policies including a ranked list of actions to perform inaccordance with one or more objectives; and performing, in response tothe content request, one or more actions of the ranked list of actionsof the identified policy.

Another aspect of the disclosure provides for one or more non-transitorycomputer-readable storage media storing instructions that when executedby one or more processors causes the one or more processors to performoperations including: receiving, from a user computing device accessinga web resource, a first content request, wherein the first contentrequest includes data characterizing user interaction by the usercomputing device with the web resource before or after receiving thefirst content request; identifying, a policy from a plurality ofpolicies including a ranked list of actions to perform in accordancewith one or more objectives; and performing, in response to the contentrequest, one or more actions of the ranked list of actions of theidentified policy.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects, features, and advantages of the presentdisclosure will be further appreciated when considered with reference tothe following description of exemplary embodiments and accompanyingdrawings, wherein like reference numerals represent like elements. Indescribing the exemplary embodiments of the disclosure illustrated inthe drawings, specific terminology may be used for the sake of clarity.However, the aspects of the disclosure are not intended to be limited tothe specific terms used.

FIG. 1 is an example computing system in accordance with embodiments ofthe disclosure.

FIG. 2 is a flow diagram of an example process for routing content inresponse to a content request among the devices shown in FIG. 1 .

FIG. 3 is a flow diagram of an example process of providing content froma content provider in response to a content request.

FIG. 4 is a flow diagram of an example process for routing content inresponse to a content request from a mobile application.

FIG. 5 is a flow diagram of an example process for generating andexecuting a policy for routing content in response to a content request.

FIG. 6 is a flow diagram of an example process for training a feedbackengine, according to aspects of the disclosure.

DETAILED DESCRIPTION Overview

This technology relates to routing web traffic to a user computingdevice in response to a content request, such as from the userinteracting with a link or URL displayed on the user computing device.

In general, a traffic routing system is configured to route content touser computing devices. The user computing devices can be configured toaccess content over a network, such as content published by a publisher,for example as part of a web page or resource. The content can beaccessed by the user computing devices by web browsers or by one or moreapplications authored or otherwise provided by the publisher foraccessing the published content. The applications used by the usercomputing devices to access content can vary, for example as anapplication built-in to an operating system running on the usercomputing devices, or as mobile applications if the user computingdevices are mobile devices like a tablet or smartphone. A web resourcecan be any data accessible and identifiable over a network, such as afile, including video files, image files, audio files, and text files,or directories of files, such as a folder of files.

A request for content, referred to herein as a content request, can bemade in response to user interaction on a user computing device. Forexample, the user computing device can register a click or touch screenpress of a link or other interactable element on a web page or resource.In response to the user interaction a content request may be generated.In other examples, the content request can be automatic, for example assoon as the user computing device is navigated to a particular web pageor resource.

The user computing device can be configured to send a content request tothe traffic routing system. For example, the content request can be sentin response to one or more user interactions with the accessed web pageor resource detected by the user computing device. As described herein,the content request can include a plurality of data pointscharacterizing the interactions leading up to or coming after thecontent request. The content request itself can include informationformatted according to any communication protocol for communicating overa network.

When a content request is made by a user computing device, the trafficrouting system can process the content request and obtain informationrelated to the content request. In some instances, subsequentinteractions between the user computing device and a publisher computingdevice may be processed. The traffic routing system includes thefeedback engine that is configured to receive the content request andinformation related to the content request, and to return a policy mostsuited for the content request according to one or more predeterminedobjectives. As described in detail herein, based on the returned policy,certain types of content can be presented through the user computingdevice, such as an advertisement or a promotion. The policy can alsospecify how the content is to be presented, for example through a mobileapplication installed on the user computing device, or a mobile webbrowser.

The additional information related to the content request can includeinformation about the user computing device, the user, the publisherand/or the content provider, as described herein. This additionalinformation can also be included as part of the plurality of data pointscharacterizing the interactions leading up to or coming after thecontent request. For example, the information related to the contentrequest can include user computing device information, such as devicetype, operating system, and operating system version, web browser and/orapplication information from which the content request was sent. Inaddition or alternatively, the information related to the contentrequest can include location information for the device, the web pagesor resources accessed by the user computing device leading up to thecontent request, or a destination URL specified by the elementinteracted with on the web page or resource that caused the usercomputing device to generate a content request.

The traffic routing system can also be configured to obtain theinformation at the time the content request is sent by the usercomputing device, as well as some time after the content request issent. Information related to interactions made after content is receivedin response to a content request can also be collected and used toimprove how subsequent content is routed, as described herein withreference to the optimization module of the feedback engine. Informationcollected after the content request is sent can include subsequent userinteraction with a web page or resource. For example, the trafficrouting system can obtain data characterizing purchases made or otherweb pages or resources visited after the content request is made andafter content responsive to the content request is sent back to the usercomputing device. In addition to purchases, other interactionsrepresented can include link clicks, redirects, such as to anapplication marketplace for downloading an application, applicationinstallations, views of different web pages or resources (such as ofdifferent products), and adding or removing items from an onlineshopping cart of a web page or resource.

The feedback engine of the traffic routing system can be configured touse this additional data to generate a map between content requestsassociated with data having certain characteristics and policies thatresult in content sent in response to the content requests that mostsatisfies predetermined objectives. In this way, the user experience ofa user operating the content requesting computing device can be improvedby receiving content that is determined by the feedback engine to be thecontent that most satisfies the objectives.

The policy can be a set of rules and/or parameters characterizing thetype of content to provide in response to the content request, which isadaptive and specific to the user computing device and/or othercharacteristics of the user, a particular publisher, and/or a particularcontent provider. The policy can specify a routing preference, forexample a preference from which sources to obtain content for the usercomputing device in response to the content request. The policy can alsospecify specific types of content.

In addition or alternatively, the policy can define additional metadatathat is provided to the user computing device that can change howcontent is displayed or otherwise presented on a display of the usercomputing device. This additional metadata can be used to alter thepresentation of content responsive to a content request, as well as tomodify the experience of a user using a browser or other application toaccess and request content.

For example, the policy can specify that certain cookies or HTTP headersbe sent in addition to the requested content, for affecting behavior ofthe application, such as a web browser used to view the content on theuser computing device.

Once the feedback engine generates a policy for routing content to theuser computing device in response to a content request, the trafficrouting system can route to the user computing device content specifiedby the policy from one or more content service computing devices. Asdescribed herein, the content provider computing devices can be serversor other devices that store content to be provided to the user computingdevices.

A publisher can be any entity or entities, such as a person, group ofpeople, business or legal entity, or software configured toautomatically post content. The content itself can be a web site withone or more web pages or resources. The content can be subdivided on aweb page presented on a user computing device, with differentsubdivisions corresponding to content originating from different contentproviders in communication with the publisher.

A publisher can maintain one or more publisher computing devices,described herein, that are configured to serve content to user computingdevices, and can also be configured to host the necessary software toview certain types of content on the user computing device, ifnecessary.

The content published by the publisher computing devices can be any formof content that can be communicated across devices coupled to a network.For example, the content can be multimedia content, such as videos,images, gifs, or live streams. In addition or alternatively, the contentcan include information about products or services for sale, includingproducts or services of different brands, as well as information aboutwhere to purchase the products or services. As examples, the content canbe content on another hosted web page. The content can be media,including text, images, and/or video. The content can also includeprompts to install applications on the user computing device. Thecontent can be an application marketplace or other source to download aparticular application, for example an application associated with aparticular brand corresponding to the link published by a publisher.

A content provider can be one or more entities, such as a person, groupof people, business or legal entity, or automated software. For example,the content provider can maintain a particular brand of product orservice, and generate content related to that brand, such asadvertisements, sale promotions, or general information about theproduct or service.

The content service computing device(s) can form at least part of anaffiliate network, and be configured to communicate with publishercomputing devices obtaining content that can be later displayed on usercomputing devices accessing a page or resource. A publisher computingdevice can be subscribed to or otherwise associated with the affiliatenetwork and receive content for publishing, for example as anadvertisement to publish on a blog.

As an example of how the traffic routing system can cause differentcontent to be sent to a user computing device in response to a contentrequest, consider user computing device A and user computing device B.In this example, user computing device A is a mobile device running amobile application published by publisher A and displaying an embeddedweb page, while user computing device B is running a native mobilebrowser to display a mobile webpage published by publisher B. On the webpage is a link indicating that the link is related to a promotion for ahotel coupon code. Both device A and device B can receive respectiveinteractions with the link, for example through touch input. Bothdevices in turn generate a content request, in this example for thehotel coupon code. In some implementations, the content request is madeautomatically, for example upon device A and device B accessing the webpage.

When device A sends the content request, the traffic routing system canprocess the content request and additional information through thefeedback engine. Part of the additional information can includeinformation relating to the fact that the content request was generatedon a publisher mobile application. In response, the feedback engine cangenerate a policy for providing the hotel coupon code to device A. Thetraffic routing system can route content related to the hotel couponcode to device A.

When device B sends the content request and additional informationindicating that the content request was not generated within thepublisher mobile application, the traffic routing system, similar to thecase of device A, can process the content request and additionalinformation. In this case, however, the feedback engine can generate apolicy that specifies that device B, in addition to the contentrequested coupon code, receives a prompt to install a mobile applicationassociated with a content provider providing the hotel coupon code.

The difference in content sent between device A and device B can beindicative of one or more objectives the feedback engine used as part ofgenerating a map between input data including a content request andadditional data, and output policies that specify content to be sent inresponse to the content request in accordance with the objective(s). Forexample, the objectives used by the feedback engine may relate to couponusage, for example an objective may be presenting a coupon in responseto a content request such that the likelihood that the coupon is appliedis maximized. As described in more detail herein, the feedback enginecan learn that a coupon sent to a mobile application is more likely tobe redeemed than a coupon sent to a mobile web browser.

In response, the feedback engine can learn a particular policy thatspecifies sending a prompt to install the mobile application, inaddition to the content requested coupon code, to a mobile devicesending a content request from a mobile web browser. On the other hand,a prompt to install a mobile application is likely not beneficial to theuser of device A, as device A already has the mobile applicationinstalled. Thus, the prompt to install the mobile application maydetract from the user experience and decrease the likelihood that thecontent requested coupon code is used. Therefore, the feedback enginegenerates a different policy in response to the content request fromdevice A that does not specify sending a prompt to install a mobileapplication.

The foregoing example illustrates how the feedback engine can adaptcontent request responses under certain conditions. As described herein,the feedback engine can be configured to learn complex maps between avariety of different inputs and corresponding policies that cannot behandpicked to the same level of granularity. Further, global policies orpolicies applied using a limited set of heuristics or handpicked rulescan become stale very quickly, and cannot be quickly adapted in responseto changes in the environment in which content is requested andreceived, including changes in user behavior, the means by which usersaccess content, or the variability of the different types of contentavailable on the internet or other large network.

Example Systems

FIG. 1 shows an example distributed computing system 102 that includes atraffic routing system 100 in which the features described herein may beimplemented. In this example, the distributed computing system 102includes the traffic routing system 100, content provider computingdevice 101, publisher computing device 103, and user computing device105, which may be collectively referred to as computing devices. Thedistributed computing system 102 may also include a storage device 109.

Communication between the computing devices 101-107, as well as betweenthe computing devices 101-105 and the traffic routing system 100implemented on the routing computing device 107, may be performedthrough network 130, as described herein. The computing devices 101-107can also communicate with the storage device 109 through the network130. FIG. 1 should not be considered as limiting the scope of thedisclosure or usefulness of the features described herein. In thisregard, the features described herein may be implemented with many typesof general or special purpose computing devices, such as personalcomputers, laptops, tablets, mobile phones, virtual computers, etc.Further, the features described herein may be implemented using manydifferent combinations of devices.

The publisher computing device and the content provider computing device101 can be part of a larger affiliate network 157. The affiliate network157 can connect content providers with publishers for providing contentto users accessing web pages or other resources published by thepublishers. The affiliate network can connect multiple content providerswith publishers through respective computing devices. The affiliatenetwork 157 can also be configured to receive, for example through oneor more devices, content requests received from the user computingdevice 105. As described herein, the affiliate network 157 can form thecontent request and include data points for processing by the feedbackengine 190. The affiliate network 157 can then forward the contentrequest with the additional information to the traffic routing system100.

The traffic routing system 100 can be implemented on the routingcomputing device 107, and/or on a plurality of computing devices acrossone or more physical locations, including the routing computing device107. The traffic routing system 100 can be part of a larger system foranalyzing interactions between users and publishers, particularlyinteraction pertaining to content provided by one or more contentproviders. In some implementations, the traffic routing system 100 is astandalone system configured according to aspects described herein. Insome implementations, the traffic routing system 100 is implemented onmultiple devices, including the routing computing device 107.

The traffic routing system 100 can include a feedback engine 190. Thefeedback engine 190 can include a data collection module 151, anoptimization module 153, and a decision delivery module 155.

In general, the data collection module 151 is configured to receive datacorresponding to the user computing device 105 and a content request bythe user computing device 105.

The data collection module 151 is configured for receiving datacorresponding to a content request from a user computing device, as wellas additional information about recorded interactions collected duringor after the content request is received. The data can be represented asmultiple data points, which can be categorized at least into threegroups: user environment, user experience, and user journey.

Data points that fall under user environment can generally refer tocharacteristics for the devices and applications from which a contentrequest is generated. Example data collected by the data collectionmodule 151 here can include user computing device information, webbrowser and/or application information from the web browser orapplication used by the user computing device to display a web page orresource published by a publisher, location information of the usercomputing device, and information about the web page or resource itself.

Device information can include the type of user computing device, suchas desktop, laptop, mobile device or wearable device. Device informationcan also include the type of hardware installed on the device, orhardware commonly associated with the device, such as the type ofprocessor on a particular brand of smartphone. Device information canalso include the type of operating system and software running on thedevice, including a respective version number for the operating systemand software. The software information can include a type of web browserused, such as a web browser built into the operating system or a webbrowser commercially or freely available by other providers. Within theweb browser, the device information may include information aboutdifferent add-ons, plug-ins, or extensions installed and running on theweb browser.

The device information can also include a unique identifier assigned toa respective user computing device, for example by a device manufacturerwhen the user computing device is built, such as a MAC address or anadvertiser identifier. The unique identifier can also be associatedper-device by software running on the device, for example the operatingsystem or an application maintained by a publisher or content provider.The feedback engine 190 can track content requests by identifier, andaggregate information for the content requests as well as additionalinformation. In some implementations, the feedback engine 190 cangenerate and associate a user computing device with a unique identifier.

The traffic routing system can also associate other devices by uniqueidentifiers, such as publisher computing devices and content providercomputing devices of the affiliate network 157. Different users can becategorized by the data collection module 151, for example according toshared characteristics, such as similar devices used to access web pagesor resources.

The device information can include information about the type ofapplication used to access a web page or resource, even if theapplication is not a web browser. For example, the device informationcan indicate that the user computing device accessed and displayed a webpage or resource as information embedded on an application speciallyconfigured for accessing web pages or resources hosted by the publisher.In some cases, the application may obtain the web page or resource asinformation called from an application programming interface (API)exposing content to be published by the publisher. The exposed contentmay be stored on a publisher computing device or a content providercomputing device of the affiliate network 157, as examples.

Data points that fall under user experience can generally refer tocharacteristics to how content is served to the user computing device105 after a content request is made. Examples include the routingexperience itself, such as whether content is provided through anapplication deep linked to the user computing device 105, contentprovided through a link to a web page or resource, and content providedthrough an application after prompting or requiring the user computingdevice 105 to install the application to view the content.

Data points falling under user experience can also include dynamicdesign elements, for example representing variations to how the samecontent is presented to the user computing device 105. As an example, adynamic design element can include a dynamic landing page for contentserved as part of a request. For some requests, the landing page canappear different than for other requests. As another example,interstitial pages, such as web pages that are presented before or aftercontent is served in response to a request, can vary, depending onpublisher.

Interstitial pages presented in response to content can also vary, forexample, based on a percentage chance of occurrence, with some pagespredetermined to occur more often than other pages. Data points based onrandomly occurring interstitial pages can also be used to characterizethe effect on user experience, for example for comparing how usersbehave in response to receiving a less commonly occurring interstitialpage versus a more commonly occurring page.

Data points that fall under user journey generally refer tocharacteristics defining interactions between the user and a usercomputing device. The data under this category can generally categorizedecisions made by the user as represented by interactions recorded aftera content request is made. Examples include link clicks, redirects,product views, products added for purchase, purchased products, andapplication installs.

The information collected by the data collection module 151 can includelocation information or information related to the time at which acontent request was sent by the user computing device. For example, thelocation information can include a geographic region or zone where theuser computing device was located at the time of the content request.The information can also include a timestamp for when the contentrequest was made by the user computing device, and the timestamp canrepresent the time local to the user computing device and/or astandardized time used consistently throughout processing by thefeedback engine 190. The timestamp can represent time according todifferent levels of granularities, for example by week, day, hour,minute, or second.

The information collected by the data collection module 151 can alsoinclude information collected that is associated with the publisher ofthe web page or resource accessed by the user computing device. Thisinformation can include a network address, hostname, or some identifierfor the publisher and one or more publisher computing devices maintainedby the publisher, either directly or indirectly.

The information collected by the data collection module 151 can alsoinclude information related to the element on the web page or resourceinteracted with to cause the user computing device to generate a contentrequest. For example, the element can be a link. The link can specify adestination, for example as a URL to a product web page. In that case,the requested content can be specific to the product web page indicatedby the link. In some cases, the link is a deep link, such as a link to aproduct in an application native to the content provider for theproduct, where the application is installed on the user computingdevice.

In some implementations, the link initially interacted with on the webpage or resource may not be a deep link, but functionally acts as a deeplink in serving content according to a policy generated by the feedbackengine to route the requested content as content displayed on the nativeapplication installed on the user computing device. On the other hand,had the link been configured as a deep link for every accessing mobiledevice, the link could be broken at least for devices that do not havethe native application installed that corresponds to the content or areotherwise unable to process the request for such a deep link.

The information collected by the data collection module 151 can alsoinclude parameter values corresponding to the content request. Forexample, a published link can include query parameter values appended toa link. The parameter values can incorporate additional information thatthe data collection module 151 can parse, such as location or timeinformation described herein.

The data collection module 151 can collect and store data points foreach interaction by different user computing devices on web pages orresources published by various publishers. The data collection module151 can parse, process, and send the data to the optimization module 153that in turn can process the data to learn different policies forserving content and modifying how the content is presented on a usercomputing device. Before passing the data, the data collection module151 can format the data according to a predetermined format accepted bythe optimization module 153. For example, the data collection module 151can generate a vector or array of multiple dimensions, in which eachelement represents one or more data points. In some implementations, thedata collection module 151 can fill in placeholder values for missingdata points, which the optimization module 153 can parse out duringprocessing described herein.

The data collection module 151 can receive the data described herein ina variety of different ways. As an example, the traffic routing system100 can define an Application Programming Interface (API) specifying howcontent requests are sent from a computing device receiving a contentrequest before redirecting it to the system 100. The API can define adata object with different fields, each corresponding to a differentdata point related to the content request. For example, the object caninclude information related to the link that was accessed as part ofgenerating the content request, including query parameters, ifapplicable, pathname, and/or hostname. Additional parameters specifiedin the object sent to the system 100 can include HTTP request headersand related fields, such as cookies, user-agent, IP address, as wellother information, such as timestamps during which the request is beingprocessed.

The data collection module 151 can also collect data from computingdevices implementing a software development kit (SDK) corresponding tothe traffic routing system 100. In general, the SDK can be configured toallow applications to be built on a communicating computing device thatincludes communicating content requests and receiving responses from thetraffic routing system 100. The SDK can implement an API that can definean object with different fields for communicating information about thecontent request, as described herein.

In some examples, the data collection module 151 can be configured toreceive content requests including data points that can be processedfurther by the data collection module. For example, some data points canbe identifiers mapped to a table or data structure stored and managed bythe traffic routing system 100. The identifiers can be specific to aparticular publisher. For example, a request can be sent as an objectwith a field containing a publisher identifier. The data collectionmodule 151 can process the publisher identifier and retrievecorresponding information according to the publisher identifier. In oneexample, the publisher identifier can map to a category of traffic ofmultiple different categories associated with the publisher. The datacollection module 151 can retrieve information related to that category,which can then be used as one or more data points for subsequentprocessing, as described here.

The optimization module 153 is configured to receive data collected bythe data collection module 151, and to output one or more policiesspecifying how content should be routed in response to a content requestfrom the user computing device 105. In general, the optimization module153 is configured to generate a map, which maps input data from theoptimization module 153 obtained in response to a content request by theuser computing device 105, to one or more policies specifying howcontent should be routed and/or presented to the user computing device105.

The mapping can be deterministic or stochastic. For example, an inputset of data points corresponding to a content request can map tomultiple different policies. The feedback engine 190 can be configuredto sample from the set of different policies, according to a learned orpredetermined sampling strategy. For example, the feedback engine 190can sample randomly between the multiple different policies. As aresult, how content is delivered and presented in response to a contentrequest can sometimes vary, even for multiple interactions andcorresponding content requests mapped to the same set of policies. Thevariation may be desired and specified, for example by a contentprovider to the traffic routing system 100 to occasionally provide for aunique user experience, such as through the inclusion of random creativecontent like videos or other animations. As another example, thevariation may correspond to providing special offers and promotions fordifferent products and services, such as to a random subset of usercomputing devices.

As another example, the sampling strategy can be learned by theoptimization module 153, as described herein. The optimization modulecan learn the sampling strategy to maximize one or more objectives. Forexample, if the content requested is of a product, by introducingvariety in how content is delivered and presented according to differentpolicies, the feedback engine 190 can improve the likelihood that theproduct is subsequently purchased, for example because the variety ofdifferent experiences in delivering and presenting the content increasesthe appeal of the product overall.

The policy can specify a ranked order or list of actions to take inresponse to a content request. The set of possible actions can bepredetermined, and the policy output by the optimization module 153 canorder the set of possible actions, from which the decision deliverymodule 155, as described herein, can cause the actions to be performedaccording to the order.

The set of possible actions in general can relate to actions for howcontent is selected, presented, and delivered to the user computingdevice 105 in response to the content request. For example, the set ofpossible actions can include:

-   1. Sending a deep link to the user computing device 105 to an    application installed on the device 105 for presenting the requested    content.-   2. Redirecting the user computing device 105 to a web page or    resource different from the web page or resource from which the user    computing device sent the content request.-   3. Causing the user computing device 105 to generate a prompt to    offer to install an application, for example an application    associated with a publisher or content provider. In some examples,    the prompt can be accompanied with the requested content.-   4. Causing the user computing device 105 to install an application    to view the content. Different from (3), above, this action can    require that content responsive to the content request not be sent    unless displayed through a particular application associated with a    publisher or content provider.-   5. Not performing any action, including the actions (1)-(4).    Instead, the content request may be allowed to timeout. In some    implementations, a “no action” includes an action to send the user    computing device 105 some indication that the content request was    not successful. Generally, a “no action” is avoided, and instead a    policy will prioritize any other response before timeout or no    response at all.

An example output policy of the routing optimization module 153 can beto rank the actions, such as those described above. For example, onepotential ranking in a policy is shown in TABLE 1, below.

TABLE 1 1 Deep link user computing device to installed application. 2Offer user computing device application installation. 3 Send usercomputing device to web page or resource. 4 Perform actions for defaultweb experience. 5 Force application installation on user computingdevice.

In the example shown in TABLE 1, an output policy can rank some actionsahead of other actions, according to one or more objectives. Forexample, the optimization module 153 can generate a map that maps inputsto output actions predicted to benefit a publisher of the web page orresource from which a content request was generated by the usercomputing device 105. A “benefit” to the publisher can be measuredaccording to different metrics and objectives corresponding to thatpublisher. One such metric can be how often a user computing device isnavigated to the content sent in response to a content request, versusleaving the web page or resource altogether and navigating to adifferent page.

As another example, the optimization module 153 can predict that forcertain types of content requests, the user computing device is morelikely to engage with the requested content if the user computing deviceis deep linked to an application installed corresponding to thepublisher. The requested content can then be presented on the installedapplication. However, absent an installed application, the policy inTABLE 1 specifies that the next ranked action is to offer to install theapplication on the device. If the offer is declined, then the next bestaction is to provide the requested content by redirecting the usercomputing device 105 to a web page or resource that includes therequested content.

In this example, the optimization module 153 has predicted a ranking ofactions that is most likely to result in engagement with the requestedcontent, and therefore meet the objective of being most “beneficial” toa publisher under this engagement metric. In other examples, the policygenerated by the optimization module 153 can change, for example fordifferent publishers, even if the objective and the metric(s) used arethe same. In some cases, the policy generated can differ for the samepublisher at different times.

As another example, TABLE 2, below, shows a different ranking in apolicy output for a content request generated from a web page orresource published by a different publisher.

TABLE 2 1 Deep link user computing device to installed application. 2Force application installation on user computing device. 3 Send usercomputing device to web page or resource. 4 Perform actions for defaultweb experience 5 Offer user computing device application installation.

Comparing TABLE 1 and TABLE 2, the actions “force applicationinstallation on user computing device” and “offer user computingapplication installation” are swapped. TABLE 2 is an example of how apolicy can change between publishers, or in some cases, change for thesame publisher at different points in time.

Routing preferences and more generally policies corresponding toobjectives for different publishers can vary over time. As a result, thefeedback engine 190 can be configured to periodically update mappingsbetween data points of content request and different policies. Theperiod at which the feedback engine 190 performs these updates can, insome implementations, be random within a predetermined interval of time,e.g. weekly or daily at different points within that interval.

For example, the feedback engine 190 at one point can determine that thebest policy for content requests from user computing devices accessing aweb resource hosted by Publisher A is policy A. More specifically andfor purposes of illustration, the feedback engine 190 can determine thatweb routing only (e.g., no prompts or links to install a mobileapplication) was generally better than other types of actions, such asprompting or linking user computing devices to install a mobileapplication for Publisher A. This determination can be based onobjectives, for example user engagement, particular to Publisher A. Inother words, the feedback engine 190 determines that user engagement isgenerally higher when web routing preferred policies are implementedover other policies.

Of note in this example is that Publisher A does not specify thispreference to the feedback engine 190. Publisher A and its correspondingpublisher computing device does not interact with the traffic routingsystem 100 to generate and update policies once the objectives for thepublisher have been established. Rather, the feedback engine 190processes content requests, including content requests originating fromuser computing devices accessing web pages or resources published byPublisher A, and evaluates different metrics corresponding to thedifferent objectives particular to the Publisher A. For example, if theobjective is user engagement, then one of the metrics evaluated can bethe frequency at which the user computing device received userinteractions for content received in response to a content request.

At a later time, Publisher A may decide to change how it presents itsweb resource to user computing devices accessing the web resource. As aresult of these changes, the feedback engine 190—which is continuing toreceive data for content requests as described herein--can determinethat user engagement for content requests responded to under Policy Ahas decreased over time. The feedback engine 190 can process incomingcontent request data and update the mapping to reflect actions of apolicy that are more likely to perform better under the objectives forPublisher A. The feedback engine 190 can determine, for example, that anew Policy B prioritizing prompting or linking user computing devices toinstall a publisher-related mobile application is preferable to the oldweb routing Policy A, at least as a result of changes made by PublisherA to its web page or resource.

Although the examples in the description of TABLE 1 and 2, herein,referred to a ranked list of actions to be performed, in someimplementations a policy can specify multiple actions that can beperformed as part of responding to a content request. Some actions canbe defined by a policy to be performed at the exclusion of others, suchas always prompting a user computing device to install an applicationinstead of forcing the installation.

Different policies that can be applied in response to a content requestcan also be generated and executed with different probabilities. Forexample, between three policies A, B, and C, policy A may be executedfor 90% of content requests, while policies B and C may be executed with5% probabilities, respectively. One reason for generating multiplepolicies with different rates of occurrence is to introduce new datapoints for measuring user interaction and experience, based on how userinteraction changes in response to different policies.

For example, a policy may occur less frequently but respond to a requestwith promotional content intended to be accessed by only a subset ofrequesting user devices. As another example, different policies maycorrespond to presenting different dynamic design elements inconjunction with requested content, such as different variations for howcontent is presented on-screen to a requesting device. One example of adynamic design element is an interstitial page. An interstitial page canbe presented, for example, before or after responding to a contentrequest with a link or redirect to the requested content. Varying therate of execution of different policies can be used to collectadditional information about the user’s interaction/experience followingthe presentation of these different interstitial pages, which can beused by the system to generate new policies as described herein.

To generate a map between inputs and policies, the optimization module153 is configured to process a large amount of information, for exampletens or hundreds of thousands of data points, and perform at least twotasks: outcome space sampling and optimizing mapped policies accordingto one or more objectives, per content request. To perform these tasks,the optimization module 153 can implement any of a variety of differentstatistical data processing techniques, including techniques usingmachine learning algorithms.

Outcome space sampling refers to determining with what probabilities tosample from different policies and/or actions within policies to performin response to the content request.

As described herein, the one or more objectives can vary depending onthe nature of the content request, for example depending on thepublisher of the web page or resource accessed by the user computingdevice, or the content provider for the content requested by the usercomputing device. Examples of objectives include different types ofconversions resulting from content received by the user computingdevice. The different types of conversions can include overallconversion rate, measured by a plurality of different conversionmetrics, such as the number of web page conversions or applicationconversions. Web page conversions and application conversions can alsobe separate objectives, for example measured by a number of clicks to aparticular web page or a number of installations on an applicationstore, respectively.

The objectives can also include threshold parameter values correspondingto different recorded conversions. For example, an objective can be tomaximize a likelihood that a user will purchase goods of a certain type,such as electronics. As another example, an objective can be to maximizeone or more measurable characteristics of recorded interactionscorresponding to a content request content, such as to maximize a numberof clicks or redirects. The objectives can include a minimum userengagement, measured overall or according to separate metrics, whichthemselves can be individual objectives. Examples here include a minimumnumber of pages viewed, a number of deep links accesses, or a number ofitems added to an online shopping cart of a web page or resourceaccessed by the user computing device.

The objectives can also include objectives related to time betweencertain interactions by a user of a user computing device with a webpage or resource. For example, one objective can be to minimize the timebetween a first interaction and a last interaction that results in thepurchase of a good or service between the user computing device and theweb page or resource. As other examples, one or more objectives can berelated to maximizing the duration a web page, resource, or specificitem of content is viewed.

As part of determining the mapping, the optimization module isconfigured to learn patterns between data points for processed contentrequests and actions performed in response to those content requests andthat correspond to high-valued metrics when evaluated according toobjectives of a given publisher. For example, the optimization modulecan be configured to learn certain patterns of data points processedbefore and after a response is provided in response to a contentrequest, and associate those patterns of data points with a high userengagement when the actions performed as part of the response areperformed. Referring back to the example herein between a web-routingPolicy A and a prompting or linking Policy B, the optimization modulecan identify patterns of data points within content requests thatperform best under Policy A versus B, according to one or moreobjectives.

The optimization module can also determine different rates to samplepolicies to maximize analyzed metrics against one or more objectives.For example, the optimization module can determine that performingactions in response to policy A with a particular probability can resultin user interactions following receiving content in response to arequest having better metrics with regard to one or more objectives, asopposed to always executing policy A at the exclusion of other policies.

Once receiving the input content request data and defining theobjectives, the mapping can be generated according to any one of avariety of different techniques. Example techniques for processing theinput data include a multi-armed bandit, Markov chain Monte Carlo, andSimulated Annealing. More than one technique can be applied together,for example random grid sampling or sparse grid sampling plus supervisedlearning techniques such as linear regression or neural networks.

For example, the optimization module can be implemented as one or moreneural networks that can be trained to map data points of a contentrequest to one or more policies. As training data, the optimizationmodule can receive multiple content requests labeled according topolicies that are determined to be the best policies to map to datapoints of the content requests, according to one or more objectives.Then, the optimization module can be trained according to any supervisedlearning technique, such as backpropagation with gradient descent, tolearn weights and/or other model parameter values that cause theoptimization module to output policies most like the ground-truthlabels. A different or same neural network can be used to also identifyprobabilities from which to sample different policies, in examples inwhich the optimization module outputs more than one policy.

Once a policy has been identified, the traffic routing system 100 canperform one or more actions in accordance with the identified policyusing the decision delivery module 155. The decision delivery module 155is configured to perform the actions described herein, and to interactwith the user computing device 105 over the network 130. In doing so,the decision delivery module 155 can provide responses to requests thatfrom the perspective of the user computing device 105, appear to comefrom the web page or resource from which the content request originated.At the same time, the publisher computing device 103 is not aware of theresponse provided by the traffic routing system 100 through the decisiondelivery module 155.

Each computing device 101-107 can include one or more processors 110,one or more memory 111, one or more storage medium 112, and/or othercomponents commonly found in general and special purpose computingdevices. Although not shown, communication between memory 111, storagemedium 112, and processor 110 may be made through one or morecommunication buses.

The one or more processors 110 can be any of a variety of differenttypes of general-purpose computing processing units (CPUs).Alternatively, or in addition to the CPUs, the processor(s) 110 can bededicated components such as a graphics processing unit (GPU), anapplication-specific integrated circuit (ASIC) or other hardware-basedprocessors, such as an ARM processor, field programmable gate array(FPGA), or a System on Chip (SoC).

Each computing device 101-107 may include computer-readable media, suchas memory 111 and storage medium 112. Computer-readable media mayinclude both volatile and nonvolatile media that is readable and/orwritable by the computing devices 101-107. For instance, computerreadable media may include disk based hard drives, solid state harddrives, hybrid hard drives,, memory cards, flash read-only memory (ROM),random access memory (RAM), DVDs, CD-ROMs, EEPROM, SD cards, externalhard drives, solid-state drives, including M.2 drives, and othermagnetic or optical storage.

Memory 111 may store and provide data and instructions that can beretrieved, executed, and/or manipulated by a processor. The memory 111can be any computer-readable media, such as ROM or RAM. The instructionsand data stored by memory 111 may be those that require immediate accessby the processor(s) 110, such as data and instructions that arecurrently being processed or operated on. In some instances, theinstructions and data stored by memory 111 may be those that arecommonly executed or processed by the processors. In this regard, delayswith retrieving the commonly executed instructions and data may bereduced compared to when the commonly executed instruction and data arestored at a more remote location, such as in storage medium 112.

The instructions may be stored in any format which may be read andexecuted by the processor and may include any executable code, such asmachine code, scripts, applications, etc. Applications may include, forinstance, an operating system (OS), mobile applications, computerapplications, etc. In some instances, instructions may include portionsof executable code, such as application modules which are part of alarger application. The data is not limited to any particular datastructure or format. For instance, the data can include individualpieces or data as well as larger data structures such as relationaldatabases, tables, extensible markup language (XML) documents, etc.Additionally, the data may be formatted in many formats such as, but notlimited to, binary values, ASCII or Unicode.

Storage medium 112 can be configured to store data and instructions. Forinstance, storage medium 112 may include applications, such as mobileapplications, computer applications, etc., as well as other data andinstructions. In some instances, the storage medium 112 may store someor all of the same data and instructions as found in memory 111. Thestorage medium 112 and/or memory 111 may also include one or moreApplication Programming Interfaces (APIs) and/or software developmentkits (SDKs). For instance and as further illustrated in FIG. 1 , thestorage medium 112 of content service computing device 101 may includeone or more APIs 114. As further illustrated in FIG. 1 , the storagemedium 112 of publisher computing device 101 and user computing device105 may include SDK 117, which may enable communication with API 114.Although SDK 117 is shown as being within application 111, SDK 117 maybe a standalone application.

Although FIG. 1 illustrates the processor 110, memory 111, storagemedium 112, and other elements of computing devices 101-105 as beingwithin the same device, the processor 110, memory 111, storage medium112, and other elements of computing devices 101-105 may be stored indifferent housings. For example, and referring to the content providercomputing device 101, the processor 110, and memory 111 may be locatedin a different housing from storage medium 112.

Accordingly, references to a processor, computer, computing device,memory, or storage medium will be understood to include references to acollection of processors, computers, computing devices, memories, orstorage mediums that may or may not operate in parallel. For example,the content provider computing device 101 may include server computingdevices. The content provider computing device 101 may be configured tooperate as a load-balanced server farm, distributed system, etc.Similarly, the publisher computing device 103 and the routing computingdevice 107 can be configured as respective servers. Yet further,although some functions described below are indicated as taking place ona single computing device having a single processor, various aspects ofthe subject matter described herein can be implemented by a plurality ofcomputing devices, for example, communicating information over thenetwork 130.

The storage device 109 can include any type of storage capable ofstoring information accessible by the content provider computing device101, the publisher computing device 103, the user computing device 105,and/or the routing computing device 107. The storage device 109 mayinclude a distributed storage device where data is stored on a pluralityof different storage devices which may be physically located at the sameor different geographic locations, such as network attached storage. Thestorage device 109 can be connected to the computing devices 101-107through the network 130 as shown in FIG. 1 , and/or may be directlyconnected to any of the computing devices 101-107. Although only asingle storage device 109 is shown in FIG. 1 , any number of storagesystems may be included in the example distributed computing system 102.In some instances, access to the storage device 109 may be limited toparticular computing devices. In some instances, one or more storagedevices may be provided for each computing device.

Each of the computing devices 101-107 can be at different nodes of anetwork 130 and capable of directly and indirectly communicating withother nodes of the network 130. Although only computing devices 101-107are depicted in FIG. 1 , it should be appreciated that a typical systemcan include a large number of connected computing devices, with eachdifferent computing device being at a node of the network 130. A node isa logical unit of computation, for example represented as a combinationof physical computing resources, as well as virtualized computingresources, such as one or more virtual machines. The network 130 andintervening nodes described herein can be interconnected using variousprotocols and systems, such that the network can be part of theInternet, World Wide Web, specific intranets, wide area networks, orlocal networks. The network 130 can support a variety of short- andlong-range connections along a variety of different bandwidths, such as2.402 GHz to 2.480 GHz, commonly associated with the Bluetooth®standard, 2.4 GHz and 5 GHz, commonly associated with the Wi-Fi®communication protocol, or with a variety of communication standards,such as the LTE® standard for wireless broadband communication. Thenetwork 130, in addition or alternatively, can also support wiredconnections between the devices 101-105, 120 including various types ofEthernet connection.

Although certain advantages may be obtained when information istransmitted or received as noted above, other aspects of the subjectmatter described herein are not limited to any particular manner oftransmission of information.

Each of the computing devices 103, 105, and 107 may be configuredsimilarly to the server computing devices 101, with one or moreprocessors, memory, and storage mediums as described above. The usercomputing device 105 can be a personal computing device intended for useby a user, and have all of the components normally used in connectionwith a personal computing device such as a central processing unit(CPU), memory storing data and instructions, a display such as display115, such as a monitor having a screen, a touch-screen, a projector, atelevision, or other device that is operable to display information, andinput device 116, such as a mouse, keyboard, touch-screen, ormicrophone. Although not shown, the devices 101, 103, and 107 may alsoinclude displays and user input devices. The computing devices 101-107may also include a network interface device, and any other componentsused for connecting these elements to one another.

Example Methods

In addition to the operations described above and illustrated in thefigures, various operations will now be described. The followingoperations do not have to be performed in the precise order describedbelow. Rather, various steps can be handled in a different order orsimultaneously, and steps may also be added or omitted.

FIG. 2 is a flow diagram of an example process 200 for routing contentin response to a content request among the devices shown in FIG. 1 . Thepublisher hosts a web page or other resource accessible to a usercomputing device, as shown in block 202. The user computing device loadsthe webpage, according to block 204, and generates a content request,according to block 206. As described herein, the content request can begenerated from an interactable element of the web page, such as a link.

The content request can be sent to the affiliate network, as shown byline 208. After receiving the content request, as shown in block 210,the affiliate network forwards the content request to the trafficrouting system, and the traffic routing system receives the contentrequest, as shown by line 212 and block 214, respectively. As part offorwarding the content request, a computing device of the affiliatenetwork is configured to receive the incoming content request andconfigure the request as a request to the traffic routing system. Therequest can be formed in accordance with an API exposing the trafficrouting system, and include fields for different data pointscharacterizing the request, as described herein.

The traffic routing system receives publisher objectives, according toblock 216. As described herein, the traffic routing system can generateone or more policies for responding to a content request, based at leastin part on the objectives of a given publisher. The traffic routingsystem can maintain separate objectives for each publisher of theaffiliate network, and generate policies accordingly. As describedherein and in reference to FIG. 6 , the traffic routing system canperiodically update generated policies in response to additionallyreceived data points from different content requests to the system.

The traffic routing system identifies a response per generated policy,as shown in block 218. The traffic routing system can apply a generatedpolicy to determine the set of actions to take in response to thecontent request. As described herein, the traffic routing system canselect one of multiple policies to execute, for example based ondifferent probabilities assigned to multiple policies responsive to thecontent request. The decision delivery module of the traffic routingsystem can be configured to receive the content request, the identifiedpolicy, and to perform actions corresponding to the content request,including serving content to the user computing device. The trafficrouting system sends content in response to the content request,according to line 220. As described herein, the response can be, forexample, in the form of a link or redirect, depending on the contentrequest and the applied policy. The user computing device can receiveand display the content in response to the request, according to block224.

FIG. 3 is a flow diagram of an example process 300 of providing contentfrom a content provider in response to a content request. The process300 can include elements of the process 200, including generating acontent request, receiving the request by the traffic routing system,and identifying a response per a policy, according to elements 202-218.Also in the process 300, the traffic routing system can send a requestto a content provider to retrieve content responsive to the contentrequest and according to the policy, as shown by line 302. In someexamples, the content responsive to a request can be content provided bya content provider, as described herein. The traffic routing system canbe configured to send a request to a computing device storing contentfrom the content provider, for example through an API request using anAPI exposing the computing device storing the content.

The content provider through a computing device can provide the contentto the traffic routing system in response to the request, according toline 302 and block 304. Then, the traffic routing system can obtain therequested content and send the content to the user computing device,according to block 306 and line 220.

FIG. 4 is a flow diagram of an example process 400 for routing contentin response to a content request from a mobile application. A mobileapplication is published, according to block 402, and the user computingdevice loads a webview on the application, according to block 404. Theuser computing device can generate a content request, according to block206 and in response to user interaction with the loaded webview, forexample in response to user interaction with a link on the webview. Theprocess 400 can include receiving the content request, identifying aresponse per a policy, and the user computing device receiving aresponse from the traffic routing system, in accordance with elements208-224 and as described herein.

FIG. 5 is a flow diagram of an example process 500 for generating andexecuting a policy for routing content in response to a content request.A system including one or more processors and one or more memorydevices, appropriately configured in accordance with the disclosure, canperform the process 500. For example, a traffic routing system, such asthe traffic routing system 100, can perform the process 500.

The traffic routing system receives a content request characterizinguser interaction at the user computing device with a web resource beforeor after receiving the content request, according to block 505. Asdescribed herein, the content request can include multiple data pointscharacterizing, for example, user environment, user experience, and userjourney. The web resource can be hosted by a publisher computing deviceand accessed by the user computing device, for example through a browseror as a web view for a dedicated mobile application associated with thepublisher. As another example, the content request can be received as adata object including a plurality of data points, such as pointscorresponding to a user of the user computing device, the user computingdevice, a publisher publishing the web resource accessed by the usercomputing device, interactions between the user computing device and apublisher computing device hosting the web resource, and contentprovided in response to the request.

The traffic routing system identifies a policy from a plurality ofpolicies, wherein the plurality of policies are generating from datacorresponding to a plurality of content requests and data characterizinguser interaction after serving a respective response to each of theplurality of content requests, according to block 510. As describedherein, the policies are mapped from data points of different contentrequests received by the traffic routing system, according to mappingsthat result in the highest metrics characterizing user interactionaccording to one or more objectives particular to the publisher of theweb resource.

The traffic routing system performs one or more actions of theidentified policy. As described herein, the policy can be a ranked listof actions that can be performed by the decision delivery module of thetraffic routing system. The decision delivery module can be configuredto perform actions with a higher ranking before performing actions of alower ranking.

FIG. 6 is a flow diagram of an example process 600 for training afeedback engine of a traffic routing system, according to aspects of thedisclosure.

The system receives data corresponding to a plurality of contentrequests and a plurality of responses to the content requests, accordingto block 605. The system can receive this data for some or all of theuser computing devices interacting with the system as part of respondingto content requests. The user computing devices can access web pages orresources hosted by different publisher computing devices, and theresponses can correspond to actions performed by the system andspecified in policies mapped to the content requests, as describedherein.

The system evaluates metrics from data points of the content requestsrepresenting user interaction between computing devices and a web pageor resource, according to block 610. As described herein, each publishercan correspond to one or more objectives, and the system can beconfigured to evaluate metrics corresponding to the objectives from datapoints of content requests.

The system determines actions of policies with the highest metricsaccording to one or more objectives, according to block 615. The systemmaps input data points of content requests to the policies of thedetermined actions, according to block 620. As described herein, theoptimization module can implement any of a variety of techniques fordetermining the mapping between data points of content requests and oneor more policies, and use metrics corresponding to objectives for aparticular publisher whose web resource was accessed by the usercomputing device in generating the request.

The subject matter described in this specification can be implemented soas to realize one or more of the following advantages or technicaleffects. Web traffic can be adaptively and accurately routed byleveraging characteristics of individual devices communicating over theopen web. A system implemented in accordance with aspects of thisdisclosure can predict a traffic route to content deemed to be the bestresponse to a particular user according to one or more predeterminedobjectives. Traffic can be accordingly routed on a per-user basis, asopposed to routing traffic using global rules. Characteristics such asthe type of device, the type of application, and available user behaviorcharacteristics can be used to predict web traffic routing rulesparticular to users of an identified type. The system can reference theindividualized web traffic routing rules to respond to a content requestwithin an acceptable latency period.

An existing traffic routing system can be augmented according to aspectsof this disclosure to provide for more accurate distribution of contentto content requesting computing devices, without requiring modificationto a user frontend for generating and sending those content requests.Different content providers can interface with the feedback engine toprovide for granular control over how their content is distributed in away not possible with existing traffic routing systems that rely onglobal rules or policies for determining how content is served to acontent requesting device.

To that end, the system can periodically update web traffic routingpolicies for different learned types of users, such as by using any of avariety of machine learning techniques. The updated policies can besaved and accessed in real-time, and the system can further keep trackof different sets of web traffic routing rules according tospecifications by different content providers. In this way, content froma content provider network can be quickly and accurately routed tomultiple different users, without disrupting the user experience, atleast because the interface by which users request content does not needto be modified.

Aspects of this disclosure can be implemented in digital circuits,computer-readable storage media, as one or more computer programs, or acombination of one or more of the foregoing. The computer-readablestorage media can be non-transitory, e.g., as one or more instructionsexecutable by a cloud computing platform and stored on a tangiblestorage device.

A computer program can be written in any type of programming language,and according to any programming paradigm, e.g., declarative,procedural, assembly, object-oriented, data-oriented, functional, orimperative. A computer program can be written to perform one or moredifferent functions and to operate within a computing environment, e.g.,on a physical device, virtual machine, or across multiple devices.

The term “engine” can refer to a software-based system or subsystem forperforming one or more functions. The engine can include one or moresoftware modules, as well as other components, which can be implementedon or more computing devices in one or more locations.

In this specification the phrase “configured to” is used in differentcontexts related to computer systems, hardware, or part of a computerprogram. When a system is said to be configured to perform one or moreoperations, this means that the system has appropriate software,firmware, and/or hardware installed on the system that, when inoperation, causes the system to perform the one or more operations. Whensome hardware is said to be configured to perform one or moreoperations, this means that the hardware includes one or more circuitsthat, when in operation, receive input and generate output according tothe input and corresponding to the one or more operations. When acomputer program is said to be configured to perform one or moreoperations, this means that the computer program includes one or moreprogram instructions, that when executed by one or more computers,causes the one or more computers to perform the one or more operations.

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter defined by the claims, the foregoingdescription of the embodiments should be taken by way of illustrationrather than by way of limitation of the subject matter defined by theclaims. In addition, the provision of the examples described herein, aswell as clauses phrased as “such as,” “including” and the like, shouldnot be interpreted as limiting the subject matter of the claims to thespecific examples; rather, the examples are intended to illustrate onlyone of many possible embodiments. Further, the same reference numbers indifferent drawings can identify the same or similar elements.

The invention claimed is:
 1. A method for updating a feedback modelcomprising: receiving, by one or more computing devices, datacorresponding to a set of content requests, the data corresponding tothe set of content request including, for each content request in theset of content requests, data characterizing interactions with arespective web resource before and after the respective content requestwas made; receiving, by the one or more computing devices, responses toeach content request of the set of content requests, wherein eachresponse includes one or more actions performed to satisfy therespective content request, wherein the one or more actions wereidentified by the feedback model in accordance with a respective policybased on at least one objective; and updating, by the one or morecomputing devices, the feedback model, wherein updating the feedbackmodel comprises: evaluating one or more metrics to determine actionsfrom the one or more actions of each response that satisfy or fail tosatisfy the at least one objective, wherein each of the one or moreactions were selected in accordance with the respective policy based onthe at least one objective; and adjusting a mapping between inputs ofthe feedback model to each of the respective policies based on datacorresponding to the determined actions that satisfy or fail to satisfythe at least one objective.
 2. The method of claim 1, wherein eachpolicy is generated using data from a plurality of received contentrequests and data characterizing user interactions with the respectiveweb resource after serving a respective response to each of theplurality of content requests.
 3. The method of claim 2, wherein theplurality of received content requests includes the set of contentrequests.
 4. The method of claim 1, wherein the data corresponding tothe set of content requests includes a plurality of data points, eachdata point corresponding to one or more of: a user of the user computingdevice, the user computing device, a publisher publishing the respectiveweb resource, interactions between the user computing device and apublisher computing device hosting the respective web resource, andcontent provided in response to the respective content request.
 5. Themethod of claim 1, wherein the at least one objective comprises one ormore of increasing user engagement, conversion rate, and repeat requestsfor the requested content.
 6. The method of claim 1, wherein theevaluated metrics comprise metrics quantifying, from the datacorresponding to a set of content requests one or more of userengagement and conversion rate for the requested content.
 7. The methodof claim 1, wherein the one or more actions includes at least one of:prompting a user computing device to install a mobile application;redirecting the user computing device to a web resource hosting contentin response to the content request; and sending a deep link to the webresource hosting content in response to the content request.
 8. Themethod of claim 1, the updating of the feedback model, wherein updatingthe feedback model occurs periodically.
 9. The method of claim 1,wherein the at least one objective is provided by a single publisher ormultiple publishers.
 10. The method of claim 1, wherein the at least oneobjective is provided by a single content provider or multiple contentproviders.
 11. The method of claim 1, wherein the data corresponding tothe determined actions that satisfy or fail to satisfy the at least oneobjective includes data corresponding to the respective content requestsof the set of content requests which resulted in the selection of thedetermined actions.
 12. A system for updating a feedback modelcomprising: one or more processors; and one or more memory devicesstoring instructions that when executed by the one or more processors,cause the one or more processors to perform operations comprising:receiving data corresponding to a set of content requests, the datacorresponding to the set of content request including, for each contentrequest in the set of content requests, data characterizing interactionswith a respective web resource before and after the respective contentrequest was made; receiving responses to each content request of the setof content requests, wherein each response includes one or more actionsperformed to satisfy the respective content request, wherein the one ormore actions were identified by the feedback model in accordance with arespective policy based on at least one objective; and updating thefeedback model, wherein updating the feedback model comprises:evaluating one or more metrics to determine actions from the one or moreactions of each response that satisfy or fail to satisfy the at leastone objective, wherein each of the one or more actions were selected inaccordance with the respective policy based on the at least oneobjective; and adjusting a mapping between inputs of the feedback modelto each of the respective policies based on data corresponding to thedetermined actions that satisfy or fail to satisfy the at least oneobjective.
 13. The system of claim 12, wherein each policy is generatedusing data from a plurality of received content requests and datacharacterizing user interactions with the respective web resource afterserving a respective response to each of the plurality of contentrequests.
 14. The system of claim 13, wherein the plurality of receivedcontent requests includes the set of content requests.
 15. The system ofclaim 12, wherein the data corresponding to the set of content requestsincludes a plurality of data points, each data point corresponding toone or more of: a user of the user computing device, the user computingdevice, a publisher publishing the respective web resource, interactionsbetween the user computing device and a publisher computing devicehosting the respective web resource, and content provided in response tothe respective content request.
 16. The system of claim 12, wherein theat least one objective comprises one or more of increasing userengagement, conversion rate, and repeat requests for the requestedcontent.
 17. The system of claim 12, wherein the evaluated metricscomprise metrics quantifying, from the data corresponding to a set ofcontent requests one or more of user engagement and conversion rate forthe requested content.
 18. The system of claim 12, wherein the one ormore actions includes at least one of: prompting a user computing deviceto install a mobile application; redirecting the user computing deviceto a web resource hosting content in response to the content request;and sending a deep link to the web resource hosting content in responseto the content request.
 19. The system of claim 12, the updating of thefeedback model, wherein updating the feedback model occurs periodically.20. The system of claim 12, wherein the at least one objective isprovided by a single publisher or multiple publishers.
 21. The system ofclaim 12, wherein the at least one objective is provided by a singlecontent provider or multiple content providers.
 22. The system of claim12, wherein the data corresponding to the determined actions thatsatisfy or fail to satisfy the at least one objective includes datacorresponding to the respective content requests of the set of contentrequests which resulted in the selection of the determined actions.